2022 Swedish Artificial Intelligence Society Workshop (SAIS)最新文献

{"title":"Ecosystem Models Based on Artificial Intelligence","authors":"Claes Strannegård, N. Engsner, J. Eisfeldt, J. Endler, A. Hansson, Rasmus Lindgren, P. Mostad, Simon Olsson, Irene Perini, Heather Reese, F. Taylan, Simon Ulfsbäcker, A. Nordgren","doi":"10.1109/sais55783.2022.9833026","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833026","url":null,"abstract":"Ecosystem models can be used for understanding general phenomena of evolution, ecology, and ethology. They can also be used for analyzing and predicting the ecological consequences of human activities on specific ecosystems, e.g., the effects of agriculture, forestry, construction, hunting, and fishing. We argue that powerful ecosystem models need to include reasonable models of the physical environment and of animal behavior. We also argue that several well-known ecosystem models are unsatisfactory in this regard. Then we present the open-source ecosystem simulator Ecotwin, which is built on top of the game engine Unity. To model a specific ecosystem in Ecotwin, we first generate a 3D Unity model of the physical environment, based on topographic or bathymetric data. Then we insert digital 3D models of the organisms of interest into the environment model. Each organism is equipped with a genome and capable of sexual or asexual reproduction. An organism dies if it runs out of some vital resource or reaches its maximum age. The animal models are equipped with behavioral models that include sensors, actions, reward signals, and mechanisms of learning and decision-making. Finally, we illustrate how Ecotwin works by building and running one terrestrial and one marine ecosystem model.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114800270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Out-of-distribution in Human Activity Recognition","authors":"Debaditya Roy, Vangjush Komini, Sarunas Girdzijauskas","doi":"10.1109/sais55783.2022.9833052","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833052","url":null,"abstract":"With the growing interest of the research community in making deep learning (DL) robust and reliable, detecting out-of-distribution (OOD) data has become critical. Detecting OOD inputs during test/prediction allows the model to account for discriminative features unknown to the model. This capability increases the model’s reliability since this model provides a class prediction solely at incoming data similar to the training one. OOD detection is well established in computer vision problems. However, it remains relatively under-explored in other domains such as time series (i.e., Human Activity Recognition (HAR)). Since uncertainty has been a critical driver for OOD in vision-based models, the same component has proven effective in time-series applications.We plan to address the OOD detection problem in HAR with time-series data in this work. To test the capability of the proposed method, we define different types of OOD for HAR that arise from realistic scenarios. We apply an ensemble-based temporal learning framework that incorporates uncertainty and detects OOD for the defined HAR workloads. In particular, we extract OODs from popular benchmark HAR datasets and use the framework to separate those OODs from the in-distribution (ID) data. Across all the datasets, the ensemble framework outperformed the traditional deep-learning method (our baseline) on the OOD detection task.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115818288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of the Hybrid Feature Learning Algorithm","authors":"M. Srihari, Zahra Gholipour, Reza Khoshkangini, Abbas Orand","doi":"10.1109/sais55783.2022.9833044","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833044","url":null,"abstract":"In recent years, machine learning (ML) algorithms have been used to minimize maintenance costs and identify problems early in the automotive sector. The determination of an asset’s residual useful life of a component at a specific time is known as “remaining useful life” (RUL). The extensive evolution of data makes it challenging to analyze and interpret high-level and valuable features from the data. The issue arises in all disciplines, and the automotive industry is no exception, given the large number of sensors to consider. Existing RUL research has not given much thought to the influence of high dimensionality data on component maintenance and deterioration. The fundamental purpose of feature selection (FS) is to select a subset of features from the data without compromising model performance. This work proposes a hybrid approach to the FS problem that combines Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). When tested on public datasets, our results demonstrate a rise in regression accuracy and a reduction in the number of selected features.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131700159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Homomorphic encryption enables private data sharing for digital health: winning entry to the Vinnova innovation competition Vinter 2021–22","authors":"Rickard Brännvall, Henrik Forsgren, H. Linge, M. Santini, Alireza Salehi, Fatemeh Rahimian","doi":"10.1109/sais55783.2022.9833062","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833062","url":null,"abstract":"People living with type 1 diabetes often use several apps and devices that help them collect and analyse data for a better monitoring and management of their disease. When such health related data is analysed in the cloud, one must always carefully consider privacy protection and adhere to laws regulating the use of personal data. In this paper we present our experience at the pilot Vinter competition 2021–22 organised by Vinnova. The competition focused on digital services that handle sensitive diabetes related data. The architecture that we proposed for the competition is discussed in the context of a hypothetical cloud-based service that calculates diabetes self-care metrics under strong privacy preservation. It is based on Fully Homomorphic Encryption (FHE) - a technology that makes computation on encrypted data possible. Our solution promotes safe key management and data life-cycle control. Our benchmarking experiment demonstrates execution times that scale well for the implementation of personalised health services. We argue that this technology has great potentials for AI-based health applications and opens up new markets for third-party providers of such services, and will ultimately promote patient health and a trustworthy digital society.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129746254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Language-agnostic Age and Gender Classification of Voice using Self-supervised Pre-training","authors":"Fredrik Lastow, Edwin Ekberg, P. Nugues","doi":"10.1109/sais55783.2022.9833071","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833071","url":null,"abstract":"Extracting speaker-dependent paralinguistic information out of a person’s voice, provides an opportunity for adaptive behaviour related to speaker information in speech processing applications. For instance, in audio-based conversational applications, adapting responses to the attributes of the correspondent is an integral part in making the conversations effective. Two speaker attributes that humans can estimate quite well, based solely on hearing a person speak, is the gender and age of that person. However, in the field of speech processing, age and gender classification are relatively unexplored tasks, especially in a multilingual setting. In most cases, hand-crafted features, such as MFCCs, have been used with some success. However, recently large transformer networks, utilizing self-supervised pre-training, have shown promise in creating general speech embeddings for various speech processing tasks. We present a baseline for gender and age detection, in both monolingual and multilingual settings, for multiple state-of-the-art speech processing models, fine-tuned for age classification. We created four different datasets with data extracted from the Common Voice project to compare monolingual and multilingual performances. For gender classification, we could reach a macro average F1 score of ~96% in both a monolingual and multilingual setting. For age classification, using classes with a size of 10 years, we obtained a macro average mean absolute class error (MACE) of 0.68 and 0.86 on monolingual and multilingual datasets, respectively. For the English TIMIT dataset, we improve upon the previous state of the art for both age regression and gender classification. Our fine-tuned WavLM model reaches a mean absolute error (MAE) of 4.11 years for males and 4.44 for females in age estimation and our fine-tuned UniSpeech-SAT model reaches an accuracy of 99.8% for gender classification. All the models were deemed fast enough on a GPU to be used in real-time settings, and accurate enough, using only a small amount of speech, to be applicable in multilingual speech processing applications.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127920155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAIS 2022 Cover Page","authors":"","doi":"10.1109/sais55783.2022.9833068","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833068","url":null,"abstract":"","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127449590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixing temporal experts for Human Activity Recognition","authors":"Debaditya Roy, Sarunas Girdzijauskas","doi":"10.1109/sais55783.2022.9833028","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833028","url":null,"abstract":"Temporal patterns are encoded within the time-series data, and neural networks, with their unique feature extraction ability, process those patterns to provide a better predictive response. Ensembles of neural networks have proven to be very effective Human Activity Recognition (HAR) tasks with time-series data, e.g., wearable sensors. The combination of predictions coming from the individual models in the ensemble helps boost the overall classification metric through efficient temporal pattern recognition. Currently, the most common strategy for combining the predictions coming from the individual models is simple averaging. However, since each ensemble model learns different temporal patterns of the time-series classification problem, a simple averaging strategy is sub-optimal. This sub-optimality is addressed in this paper through a neural network-based adaptive learning framework. The method’s core is training a neural gate that ingests the same input time-series data fed to the other temporal models. The goal of the training process is to adaptively learn scaler values against each temporal model by looking at the input data. These scaler values weigh each temporal model while combining the ensemble. The framework obtains superior predictive performance as compared to the standard ensembling techniques. The framework is evaluated on a benchmark HAR dataset called PAMAP2 [3] with two popular state-of-the-art ensemble architectures namely DTE [1] and LSTM-ensemble [2]. In both cases, the classification performance of the framework in HAR tasks surpasses the state-of-the-art models.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115979813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Siambert: Siamese Bert-based Code Search","authors":"Francisco J. Pena, Angel Luis Gonzalez, Sepideh Pashami, A. Al-Shishtawy, A. H. Payberah","doi":"10.1109/sais55783.2022.9833051","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833051","url":null,"abstract":"Code Search is a practical tool that helps developers navigate growing source code repositories by connecting natural language queries with code snippets. Platforms such as StackOverflow resolve coding questions and answers; however, they cannot perform a semantic search through the code. Moreover, poorly documented code adds more complexity to search for code snippets in repositories. To tackle this challenge, this paper presents Siambert, a BERT-based model that gets the question in natural language and returns relevant code snippets. The Siambert architecture consists of two stages, where the first stage, inspired by Siamese Neural Network, returns the top K relevant code snippets to the input questions, and the second stage ranks the given snippets by the first stage. The experiments show that Siambert outperforms non-BERT-based models having improvements that range from 12% to 39% on the Recall@1 metric and improves the inference time performance, making it 15x faster than standard BERT models.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121682281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magic in Human-Robot Interaction (HRI)*","authors":"M. Cooney, A. Vinel","doi":"10.1109/sais55783.2022.9833054","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833054","url":null,"abstract":"“Magic” is referred to here and there in the robotics literature, from “magical moments” afforded by a mobile bubble machine, to “spells” intended to entertain and motivate children–but what exactly could this concept mean for designers? Here, we present (1) some theoretical discussion on how magic could inform interaction designs based on reviewing the literature, followed by (2) a practical description of using such ideas to develop a simplified prototype, which received an award in an international robot magic competition. Although this topic can be considered unusual and some negative connotations exist (e.g., unrealistic thinking can be referred to as magical), our results seem to suggest that magic, in the experiential, supernatural, and illusory senses of the term, could be useful to consider in various robot design contexts, also for artifacts like home assistants and autonomous vehicles–thus, inviting further discussion and exploration.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128182672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Slope and Generalization Properties of Neural Networks","authors":"Anton Johansson, N. Engsner, Claes Strannegård, P. Mostad","doi":"10.1109/sais55783.2022.9833034","DOIUrl":"https://doi.org/10.1109/sais55783.2022.9833034","url":null,"abstract":"Neural networks are very successful tools in for example advanced classification. From a statistical point of view, fitting a neural network may be seen as a kind of regression, where we seek a function from the input space to a space of classification probabilities that follows the “general” shape of the data, but avoids overfitting by avoiding memorization of individual data points. In statistics, this can be done by controlling the geometric complexity of the regression function. We propose to do something similar when fitting neural networks by controlling the slope of the network.After defining the slope and discussing some of its theoretical properties, we go on to show empirically in examples, using ReLU networks, that the distribution of the slope of a well-trained neural network classifier is generally independent of the width of the layers in a fully connected network, and that the mean of the distribution only has a weak dependence on the model architecture in general. We discuss possible applications of the slope concept, such as using it as a part of the loss function or stopping criterion during network training, or ranking data sets in terms of their complexity.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126065280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}